Reservoir and lake sedimentation is a vital problem in Ethiopia. Constructing small and medium size dams at the outlets of sub-catchments within larger catchment helps to reduce the transport of sediment downstream to reservoirs or lakes. This study assessed the sediment trapping efficacy (STE) of sediment storage dams (SSDs) built at the outlets of eight small sub-catchments in northwest Ethiopia, as an off-site sediment trapping measure. Satellite imagery and topographic maps were used to assess land use/land cover and delineate the boundaries of sub-catchments. In the field, trapped sediment by SSDs was measured directly, as well as in- and outflow of suspended sediment with which the STE of each SSD was estimated. Sediment yield of each sub-catchment was calculated from the measured trapped sediment and estimated suspended sediment loss. Results show that SSDs trapped an average of 1584 t yr-1 of the inflow sediment and catchment specific sediment yield ranged from 8.6-55 t ha-1 yr-1. Two representative SSDs constructed from gabion and stone were evaluated with regard to their STE. Results showed that their efficacy was 74% and 67% for the gabion and stone SSD, respectively. In general, although SSDs might be costly for small scale farmers and have a relatively short life span depending on their size, they are promising off-site structural measures to trap significant amounts of sediment at the outlets of sub-catchments and subsequently reducing sediment movement to downstream water bodies.

Research-based evidence on the effects of soil and water conservation practices (SWCPs) on soil physicochemical properties and crop yield is vital either to adopt the practices or design alternative land management strategies. Thus, this study was conducted to evaluate the effects of about 10-year-old soil bund (SB) and stone-faced soil bund (SFSB) structures on selected soil physicochemical properties, slope gradient, barley grain yield, and yield components in the Lole watershed, in the northwest highlands of Ethiopia. The experiment consisted of three treatments: (i) fields treated with SB, (ii) fields treated with SFSB, and (iii) fields without conservation practices (control) with three replications at three slope classes. A total of 27 composite soil samples from 0 to 20 cm depth and barley grain yield samples from 27 locations were collected. The soil samples were analyzed for bulk density, soil texture, porosity, soil reaction, cation exchange capacity, organic carbon, total nitrogen, available phosphorous, and potassium. Barley grain yield was analyzed using different agronomic parameters. The result indicated that SB and SFSB positively influenced the physicochemical properties of soils and barley grain yield. The interslope gradient between the successive SBs and SFSBs was reducing. Moreover, the untreated fields showed significantly lower barley grain yield, plant height, and straw biomass. Hence, SB and SFSB practices were found to be effective in changing slope gradient, improving soil fertility, and increasing crop yield. Therefore, this finding is vital to create awareness and convince farmers to construct SWCPs on their farmlands for sustainable land management.

Lake Tana is Ethiopia’s largest lake and is infested with invasive water hyacinth (E. crassipes), which endangers the lake’s biodiversity and habitat. Using appropriate remote sensing detection methods and determining the seasonal distribution of the weed is important for decision-making, water resource management, and environmental protection. As the demand for the reliable estimation of E. crassipes mapping from satellite data grows, comparing the performance of different machine learning algorithms could help in identifying the most effective method for E. crassipes detection in the lake. Therefore, this study aimed to examine the ability of random forest (RF), support vector machine (SVM), and classification and regression tree (CART) machine learning algorithms to detect E. crassipes and estimating seasonal spatial coverage of the weed on the Google Earth Engine (GEE) platform using Landsat 8 and Sentinel 2 images. Cloud-masked monthly median composite Landsat 8 and Sentinel 2 data from October 2021 and 2022, January 2022 and 2023, March 2022, and June 2022 were used to represent autumn, winter, spring, and summer, respectively. Four spectral indices were derived and used in combination with spectral bands to improve the E. crassipes detection accuracy. All methods achieved greater than 95% and 90% overall accuracy when using Sentinel 2 and Landsat 8 images, respectively. Using both data sets, all methods achieved a greater than 93% F1 score for E. crassipes detection. Though the difference in performance between the methods was small, the RF was the most accurate, while the SVM and CART methods had the same accuracy. The maximum E. crassipes coverage area was observed in autumn (22.4 km²), while the minimum (2.2 km²) was observed in summer. Based on Sentinel 2 data, the E. crassipes area coverage decreased significantly by 62.5% from winter to spring and increased significantly by 81.7% from summer to autumn. The findings suggested that the RF classifier was the most accurate E. crassipes detection algorithm, and autumn was an appropriate season for E. crassipes detection in Lake Tana.